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Abstract Crop protection is heavily dependent on the use of chemical pesticides that are not only toxic but also detrimental to the environment. To minimize the chemical approaches of crop protection, this study investigates a natural protease inhibitor PCPI (Pleiogynium cerasiferum protease inhibitor) extracted from the seeds of Pleiogynium cerasiferum as a viable bioresource with biopesticidal properties. PCPI demonstrated inhibitory activity of 92% against bovine trypsin and 89% against Helicoverpa armigera's midgut proteolytic enzymes. Characterization of PCPI revealed a 24 kDa protein with critical inhibitory concentrations of 1.53 µg/ml for H. armigera midgut proteases and 1.31 µg/ml for bovine trypsin. At the concentration of 0.5% (w/w) of PCPI, the deformities in pupae were ~ 82%, adult insects ~ 87%, and egg-laying as well as egg hatching capacity dropped by ~ 81% and 17% respectively. Principal component analysis (PCA), complemented by hierarchical clustering, was applied to assess the nuanced effects of PCPI across the pest's lifecycle. The analysis identified deformities in pupae, adult, adult emergence, and notably, egg laying reduction as variables most responsive to PCPI concentrations. Hierarchical clustering analysis corroborated these PCA findings indicating a concentrated influence on developmental stages and reproduction metrics, signifying the multifaceted impact of PCPI. These insights affirm PCPI's promise as a cornerstone of eco-friendly pest management strategies.

AbstractThe line transect survey method is commonly used to estimate the population size. However, recent developments in this field have tended to prefer practical mathematical models for this purpose. In this study, a new model called the weighted Weibull detection model was introduced to specifically address certain criteria related to line transect data. This study thoroughly explored the characteristics of this new model, including its shapes, moments, and probability density functions. The maximum likelihood estimation and Bayesian estimation methods were employed to ensure accurate parameter estimation. To gauge the performance of this model, population size estimates were generated and compared with various existing parametric estimation methods that are commonly used in the field. Through simulations, the resulting estimates were assessed and compared to widely adopted approaches for estimating population size. Additionally, this mathematical model was applied to real-world data involving perpendicular distances, allowing for a direct comparison of its performance against both traditional and contemporary methods using various measures of goodness of fit. Moreover, the study calculates statistical metrics, such as the variance–covariance matrix, parameter confidence intervals, and estimated population size, were obtained using the proposed detection model. These metrics provide valuable insights into the precision and uncertainty associated with estimated parameters and population size estimates. Confidence intervals offer a range of plausible parameter values, whereas the variance–covariance matrix quantifies the relationships and uncertainties between the estimated parameters.

AbstractThis study focuses on implementing a group acceptance sampling plan (GASP) for simultaneous inspection of objects with complex lifespan patterns described by the odd exponential logarithmic (OEL) Fréchet distribution. The inspiration from the proposed model stems from the challenges posed by the objects that exhibited right-skewed, j-shaped, reversed j-shaped and nearly symmetric densities, along with decreasing and increasing hazard rates. A mathematical function is derived to identify key points in the distribution, crucial for designing the inspection process based on factors like testing duration and acceptable risk levels. Performance is assessed across various quality levels, with a simulation study providing insights into parameter changes. The results are clarified via visual representations. The minimum ratios of actual to expected lifespan at specific risk levels, utilizing real-world data to validate the approach are computed. Finally, a comparison is driven in the quest of highlighting the efficacy and versatility of the proposed acceptance sampling plan (ASP) under the inverse log-logistic and generalized exponential (GE) models.

ABSTRACT Background Previously, most researchers explored the association between chronic obstructive pulmonary disease (COPD) and cardiovascular disease (CVD). This study investigates the distinct influence of COVID‐19 vaccination status on patients with both conditions. Objective We investigate the relationship between COPD and CVD in a cohort of 838 individuals who presented with both conditions. Our aim is to understand how these conditions interact and how COVID‐19 vaccination status affects patient outcomes. Methods A retrospective analysis was conducted on 838 patients with COPD and CVD treated at DHQ Hospital in Muzaffargarh, Punjab, Pakistan, from November 2022 to April 2023. We employed multiple logistic regression and the Wilcoxon signed‐rank test to assess the odds ratio and relative risk of COPD in patients with‐CVD under various conditions. Additionally, we analyzed time‐to‐death and survival using Kaplan–Meier methods. Results Findings reveal a 7.95 times higher risk of death in patients without COVID‐19 vaccination compared with those who were vaccinated (95% CI, 6.12–10.33). Conversely, COVID‐19‐vaccinated patients exhibited a 0.221 times lower risk of recovery than their nonvaccinated counterparts (95% CI, 0.08–0.60). We also observed significant differences in time‐to‐death and recovery based on the presence of COPD and CVD, with vaccinated patients generally experiencing milder disease. Conclusion Our study assessed the impact of COVID‐19 vaccination status on patient outcomes in patients with overlapping COPD and CVD. Individuals diagnosed with COPD and CVD display significant differences in terms of their probability of survival, with those who have received vaccinations.

Abstract This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.

Abstract In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

This paper develops a novel two-parameter unit probability model which is the generalized form Kumaraswami distribution that exhibits greater flexibility compared to well-known existing distributions, attributed to its distinct hazard and density function shapes. Extensive analysis has been conducted to explore numerous statistical features of the specified distribution, specifically moments, and order statistics providing explicit expressions for these measures. The maximum likelihood estimation is employed to estimate the model parameters and a numerical simulation analysis confirms the consistency of this estimation approach. Furthermore, the applicability of the specified model is demonstrated by considering four real data sets, showcasing its effectiveness in capturing the characteristics of real life data. The proposed model shows promise as a versatile tool for analyzing diverse data sets in a wide range of fields.

This paper introduces a novel Poisson-unit-Weibull (PUW) distribution, which is defined on a unit domain and characterized by three parameters. The PUW distribution is capable of accommodating diverse non-monotone failure rates. The paper explores several significant statistical properties of the model, including the explicit closed-form expressions for the $r^{th}$ moments, quantile function, and Shannon entropy. The parameters of the PUW distribution are estimated using maximum likelihood estimation (MLE) and Bayes estimation with a square error loss function. The performance of these estimation methods is evaluated through Monte Carlo simulation studies. Furthermore, the paper discusses the practical aspects of the PUW-quantile regression model and its MLE, employing residual analysis in simulation studies. The flexibility of the PUW and PUW-quantile regression model is demonstrated through six real-life applications, showcasing their superior performance when compared to other popularly used models.

This paper studies the flexible Lomax distribution's order statistics with graphical and numerical findings. Along with the quantitative measurements, some plots are furnished, including those for the skewness and kurtosis measures. We will dwell on the numerous results that relate to statistics of moments of order. We consider the single and product moment of order statistics from the new distribution. Further, we establish some recurrence relation for single moments of order statistics. We have sought to apply the derived relations to empirically evaluate the moments of smallest (largest) order statistics to establish well-known moments and related measures. For order statistics of a flexible Lomax distribution, exact analytical expressions of entropy, residual entropy, and past latent entropy are determined. (original abstract)

Abstract Background Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches. Methodology To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series). Results The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts. Conclusion In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

Intellectual disability (ID) is a progressive disorder that affects around 1–3% of the world's population. The heterogeneity of intellectual disability makes it difficult to diagnose as a complete disease. Genetic factors and major mutations play a noticeable role in the development and progression of ID. There is a high need to explore novel variants that may lead to new insights into the progressive aspects of ID. In the current course of study, 31 samples of ID from different studies available on GEO (GSE77742, GSE74263, GSE90682, GSE98476, GSE108887, GSE145710, and PRJEB21964) datasets were taken for the study. These datasets were analyzed for differential gene expression and single nucleotide polymorphism (SNPs). The SNPs of high impact were compared with the differentially expressed genes. Comparison leads to the identification of the priority gene ie NPR3 gene. The identified priority gene further was evaluated for the effect of the mutation using a Mutation Taster. Structure comparison analysis of the wild and mutated proteins of the NPR3 gene was further carried out by UCSF Chimera. Structural analysis reveals the anomalies in protein expression affecting the regulations of the NPR3 gene. These findings identified a novel nonsense mutation (E222*) in the downregulated NPR3 gene that leads to anomalies in the regulation of its protein expression. This missense mutation reveals a major role in causing ID. Our study concludes that the decrease in the expression of the NPR3 gene causes delayed sensory, motor, and physiological functions of the human brain leading to neurodevelopmental delay that causes ID.

The premise of extreme value theory focuses on the stochastic behaviour and occurrence of extreme observations in an event that is random. Traditionally for univariate case, the behaviour of the maxima is described either by the types-I, types-II or types-III extreme value distributions, primarily known as the Gumbel, Fréchet or reversed Weibull models. These are all particular cases of the generalized extreme value (GEV) model. However, in real-world scenario, these incidents take place as a consequence of concurrent dependent random events, where the relationship between the two variables is unidirectional or asymmetrical. [1] introduced a rigorous univariate extension of GEV distribution involving an additional parameter, the q− generalized extreme value (q−GEV) distribution, as well as the q− Gumbel distribution. The prime interest of this paper lies in conceptualizing a novel approach to model bi-variate (EV) data, arising naturally from independent q−GEV random variables. This is achieved via the transformation of variables technique by establishing the resulting supports. Concisely, a technique is developed to model interdependent bivariate observations consisting of extreme values in terms of q−GEV probability density functions. Besides, we employed the suggested technique to a bivariate flood data set and demonstrate the competitiveness of the proposed bivariate q−GEV. Additionally, conventional method to propose the newly defined bivariate (q−GEV) distribution with bivariate q− Gumbel distribution (a special case for ξ→0) has also been established with related inferences and application to climate data.

AbstractBackground The exhaustive information about non-communicable diseases associated with COVID-19 and severe acute respiratory syndrome corona virus-2 (SARS-CoV-2) are getting easier to find in the literature. However, there is a lack of knowledge regarding tuberculosis (TB) and chronic obstructed pulmonary disease (COPD), with numerous infections in COVID-19 patients.Objectives Priority is placed on determining the patient’s prognosis based on the presence or absence of TB and COPD. Additionally, a comparison is made between the risk of death and the likelihood of recovery in terms of time in COVID-19 patients who have either COPD or TB.Methodology At the DHQ Hospital in Muzaffargarh, Punjab, Pakistan, 498 COVID-19 patients with TB and COPD were studied retrospectively. The duration of study started in February 2022 and concluded in August 2022. The Kaplan–Meier curves described time-to-death and time-to-recovery stratified by TB and COPD status. The Wilcoxon test compared the survival rates of people with TB and COPD in two matched paired groups and their status differences with their standard of living.Results The risk of death in COVID-19 patients with TB was 1.476 times higher than in those without (95% CI: 0.949–2.295). The recovery risk in COVID-19 patients with TB was 0.677 times lower than in those without (95% CI: 0.436–1.054). Similarly, patients with TB had a significantly shorter time to death (p=.001) and longer time to recovery (p=.001).Conclusions According to the findings, the most significant contributor to an increased risk of morbidity and mortality in TB and COPD patients was the COVID-19.KEY MESSAGESSARS-Cov-19 is a new challenge for the universe in terms of prevention and treatment for people with tuberculosis and chronic obstructive pulmonary disease, among other diseases.Propensity score matching to control for potential biases.Compared to hospitalized patients with and without (TB and COPD) had an equivalently higher mortality rate.

The exponential distribution is one of the most widely used statistical distribution for reliability issues. In this paper, we introduce a novel family based on the exponential model, called the new exponential-H (NEx-H) family. The sub-models of the NEx-H family are capable of accommodating variable failure rates, as well as unimodal, bimodal, left-skewed, symmetric, right-skewed, and J-shape densities. The mathematical features of the NEx-H family are derived. The parameters of the NEx-Weibull distribution are estimated by using seven estimation methods. Detailed numerical simulations are presented. Based on our study, the maximum likelihood is the best estimation method for estimating the NEx-Weibull parameters. Three real-life data sets are fitted using the NEx-Weibull distribution. The NEx-Weibull model provides better fit as compared to some competing Weibull models.

Intellectual disability (ID) is an early childhood neurodevelopmental disorder that is characterized by impaired intellectual functioning and adaptive behavior. It is one of the major concerns in the field of neurodevelopmental disorders across the globe. Diversified approaches have been put forward to overcome this problem. Among all these approaches, high throughput transcriptomic analysis has taken an important dimension. The identification of genes causing ID rapidly increased over the past 3 to 5 years owing to the use of sophisticated high throughput sequencing platforms. Early monitoring and preventions are much important for such disorder as their progression occurs during fetal development. This study is an attempt to identify differentially expressed genes (DEGs) and upregulated biological processes involved in development of ID patients through comparative analysis of available transcriptomics data. A total of 7 transcriptomic studies were retrieved from National Center for Biotechnology Information (NCBI) and were subjected to quality check and trimming prior to alignment. The normalization and differential expression analysis were carried out using DESeq2 and EdgeR packages of Rstudio to identify DEGs in ID. In progression of the study, functional enrichment analysis of the results obtained from both DESeq2 and EdgeR was done using gene set enrichment analysis (GSEA) tool to identify major upregulated biological processes involved in ID. Our findings concluded that monitoring the level of E2F targets, estrogen, and genes related to oxidative phosphorylation, DNA repair, and glycolysis during the developmental stage of an individual can help in the early detection of ID disorder.

BackgroundGross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country.MethodologyStochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model.ResultsThe findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023.ConclusionThe selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

AbstractIn this paper, we propose a tractable Kumaraswamy Bell exponential (KwBE) distribution as a submodel of the Kumaraswamy Bell-G family of distributions. Several well-established properties are obtained for the KwBE distribution, such as the linear functional representation, [Formula: see text]th moment, incomplete moment, moment generating function using Wright generalized hyper-geometric function, conditional moment and Réyni entropy. Based on the KwBE model, a group acceptance sampling plan (GASP) for the truncated life test is presented using median life as a quality index. Moreover, the essential design parameters are derived by defining the consumer risk and the test termination duration. The comparative study of GASP with ordinary sampling plan (OSP) is performed. A simulation study is performed to highlight the behavior of the estimates. On the inferential side, the associated parameters are estimated using a well-established maximum likelihood estimation method. The detailed model’s comparison analysis, graphical as well as numerical evidence to real-data applications, supports the theoretical work.

AbstractIn this study, we presented a group acceptance sampling plan for situations in which the lifetime of an item followed a flexible new Kumaraswamy exponential distribution for applications to quality control reliability. This study includes a detailed analysis of the operating characteristic function values, producer risk, and minimum required group size for the acceptance number, which are determined by three shape parameters. To determine the quality index, we used the median and tested various parametric values to obtain the optimized values. These optimized values are presented in the tables and graphically for a better understanding. In addition, we explain the results of our study using two real-life datasets and an example. Comparison has been also made between GASP and OSP.

AbstractIn the present study, a group-acceptance sampling plan was presented when the lifetime of an item followed an alpha power transformation-inverted perks distribution. The median is used as a quality index to attain design parameters, such as the acceptance number and minimum group size, which are determined when the test termination and consumer risk are specified. The operating characteristic values are presented both graphically and in the form of tables. Two real-life datasets are used as examples to illustrate the tables.

In this article, the odd Lomax Gompertz distribution has been introduced, which is derived by modifying the Gompertz distribution to serve as a baseline model in the odd generalized Lomax distribution. The newly proposed model offers enhanced flexibility and provides a promising alternative for modeling lifetime data. This study seeks to establish a solid theoretical foundation for its application through the exploration of several properties, such as non-central moments, stochastic orderings quantile function, and entropy measure, for the new model. Additionally, by conducting simulation analysis, the performance of the various estimation methods is being assessed, which enables the identification of the most reliable approach for estimating the unknown parameters of the newly developed model. The simulation analysis of the two-risk metrics, namely, value at risk and expected shortfall, revealed the ability of the distribution to capture diverse failure rate patterns, which makes it particularly relevant for assessing financial risks. Finally, the suggested model is practiced to two real-life datasets to provide the compelling evidence of superior flexibility and practical versatility compared to existing models in the literature.

We focus on a specific sub-model of the proposed family that we call the new half logistic- Fréchet. This sub-model stems from a new generalisation of the half-logistic distribution which we call the new half-logistic-G. The novelty of proposing this new family is that it does not include any additional parameters and instead relies on the baseline parameter. Standard statistical formulas are used to show the forms of the density and failure rate functions, ordinary and incomplete moments with generating functions, and random variate generation. The maximum likelihood estimation procedure is used to estimate the set of parameters. We conduct a simulation analysis to ensure that our calculations are converging with lower mean square error and biases. We use three real-life data sets to equate our model to well-established existing models. The proposed model outperforms the well- established four parameters beta Fréchet and exponentiated generalized Fréchet for some reallife results, with three parameters such as half-logistic Fréchet, exponentiated Fréchet, Zografos-Balakrishnan gamma Fréchet,Topp-Leonne Fréchet, and Marshall-Olkin Fréchet and two-parameter classical Fréchet distribution. (original abstract)

This article introduces a new flexible four parameter distribution by convolution of the exponential and Weibull distribution using the odd function transformation, which offers greater flexibility in terms of fit, its called the modified exponential-Weibull (MEW). The MEW model is designed to provide a more accurate description of failure time data resulting from a system with one or more failure modes and is characterized by a hazard rate (HR) that takes the shape of a bathtub due to its complexity. The moments properties, quantile function, and residual life are derived and discussed. We discussed the HR function and several distributional properties of the MEW model, and applied maximum likelihood and Bayesian techniques to estimate its unknown parameters. The Hamiltonian Monte Carlo (HMC) algorithm is employed to simulate the posterior distributions and verify the MEW Bayes estimators. We examined the behavior of the MEW model on two data sets with bathtub-shaped HR and compare it with five other popular bathtub-shaped methodologies. The results indicate that the MEW model provided the best description of the two failure time data sets, suggesting that the proposed model could be a viable candidate for solving various real-life problems.

In this article, we make mathematical and practical contributions to the Bell-X family of absolutely continuous distributions. As a main member of this family, a special distribution extending the modeling perspectives of the famous Burr XII (BXII) distribution is discussed in detail. It is called the Bell-Burr XII (BBXII) distribution. It stands apart from the other extended BXII distributions because of its flexibility in terms of functional shapes. On the theoretical side, a linear representation of the probability density function and the ordinary and incomplete moments are among the key properties studied in depth. Some commonly used entropy measures, namely Rényi, Havrda and Charvat, Arimoto, and Tsallis entropy, are derived. On the practical (inferential) side, the associated parameters are estimated using seven different frequentist estimation methods, namely the methods of maximum likelihood estimation, percentile estimation, least squares estimation, weighted least squares estimation, Cramér von-Mises estimation, Anderson-Darling estimation, and right-tail Anderson-Darling estimation. A simulation study utilizing all these methods is offered to highlight their effectiveness. Subsequently, the BBXII model is successfully used in comparisons with other comparable models to analyze data on patients with acute bone cancer and arthritis pain. A group acceptance sampling plan for truncated life tests is also proposed when an item's lifetime follows a BBXII distribution. Convincing results are obtained.

In this paper, we propose a novel family of distributions called the odd Muth-G distributions by using Transformed-Transformer methodology and study their essential properties. The distinctive feature of the proposed family is that it can provide numerous special models with significant applications in reliability analysis. The density of the new model is expressible in terms of linear combinations of generalized exponentials, a useful feature to extract most properties of the proposed family. Some of the structural properties are derived in the form of explicit expressions such as quantile function, moments, probability weighted moments and entropy. The model parameters are estimated following the method of maximum likelihood principle. Weibull is selected as a baseline to propose an odd Muth-Weibull distribution with some useful properties. In order to confirm that our results converge with minimized mean squared error and biases, a simulation study has been performed. Additionally, a plan acceptance sampling design is proposed in which the lifetime of an item follows an odd Muth-Weibull model by taking median lifetime as a quality parameter. Two real-life data applications are presented to establish practical usefulness of the proposed model with conclusive evidence that the model has enough flexibility to fit a wide panel of lifetime data sets.

The short, non-coding RNAs known as miRNA modulate the expression of human protein-coding genes. About 90 % of genes in humans are controlled by the expression of miRNA. The dysfunction of these miRNA target genes leads to many human diseases, including neurodevelopmental disorders as well. Intellectual disability (ID) is a neurodevelopmental disorder that is characterized by adaptive behavior and intellectual functioning which includes logical reasoning, ability in learning, practical intelligence, and verbal skills. Identification of miRNA involved in ID and their associated target genes can help in the identification of diagnostic biomarkers related to ID at a very early age. The present study is an attempt to identify miRNA and their associated target genes that play an important role in the development of intellectual disability patients through the meta-analysis of available transcriptome data. A total of 6 transcriptomic studies were retrieved from NCBI and were subjected to quality check and trimming before alignment. The normalization and identification of differentially expressed miRNA were carried out using the EdgeR package of R studio. Further, the gene targets of downregulated miRNA were identified using miRDB. The system biology approaches were also applied to the study to identify the hub target genes and the diseases associated with main miRNAs.

The half-logistic (HL) distribution is a widely considered statistical model for studying lifetime phenomena arising in science, engineering, finance, and biomedical sciences. One of its weaknesses is that it has a decreasing probability density function and an increasing hazard rate function only. Due to that, researchers have been modifying the HL distribution to have more functional ability. This article provides an extensive overview of the HL distribution and its generalization (or extensions). The recent advancements regarding the HL distribution have led to numerous results in modern theory and statistical computing techniques across science and engineering. This work extended the body of literature in a summarized way to clarify some of the states of knowledge, potentials, and important roles played by the HL distribution and related models in probability theory and statistical studies in various areas and applications. In particular, at least sixty-seven flexible extensions of the HL distribution have been proposed in the past few years. We give a brief introduction to these distributions, emphasizing model parameters, properties derived, and the estimation method. Conclusively, there is no doubt that this summary could create a consensus between various related results in both theory and applications of the HL-related models to develop an interest in future studies.

Abstract Background Accessibility to the immense collection of studies on noncommunicable diseases related to coronavirus disease of 2019 (COVID‐19) and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) is an immediate focus of researchers. However, there is a scarcity of information about chronic obstructed pulmonary disease (COPD), which is associated with a high rate of infection in COVID‐19 patients. Moreover, by combining the effects of the SARS‐CoV‐2 on COPD patients, we may be able to overcome formidable obstacles factors, and diagnosis influencers. Materials and Methods A retrospective study of 280 patients was conducted at DHQ Hospital Muzaffargarh in Punjab, Pakistan. Negative binomial regression describes the risk of fixed successive variables. The association is described by the Cox proportional hazard model and the model coefficient is determined through log‐likelihood observation. Patients with COPD had their survival and mortality plotted on Kaplan–Meier curves. Results The increased risk of death in COPD patients was due to the effects of variables such as cough, lower respiratory tract infection (LRTI), tuberculosis (TB), and body‐aches being 1.369, 0.693, 0.170, and 0.217 times higher at (95% confidence interval [CI]: 0.747–1.992), (95% CI: 0.231–1.156), (95% CI: 0.008–0.332), and (95% CI: −0.07 to 0.440) while it decreased 0.396 in normal condition. Conclusion We found that the symptoms of COPD (cough, LRTI, TB, and bodyaches) are statistically significant in patients who were most infected by SARS‐CoV‐2.

This article deals with the estimation of the finite population mean under probability proportional to size (PPS) sampling using information on the auxiliary variable along with the rank of the auxiliary variable. We propose a ratio, product and regression type estimators by incorporating the maximum and minimum values of the study variable and the auxiliary variable. The mathematical expressions of the proposed estimators are derived up to first order of approximation. Efficiency comparisons are made on the basis of real data sets.

This article introduces a new three-parameter Marshall-Olkin Burr-R (MOB-R) family which extends the generalize Burr-G class. Some of its general properties are discussed. One of its special models called the MOB-Lomax distribution is studied in detail for illustrative purpose. A modified chi-square test statistic is provided for right censored data from the MOB-L distribution. The model parameters are estimated via the maximum likelihood and simulation results are obtained to assess the behavior of the maximum likelihood approach. Applications to real data sets are provided to show the usefulness of the proposed MOB-Lomax distribution. The modified chi-square test statistic shows that the MOB-Lomax model can be used as a good candidate for analyzing real censored data.

We propose a new flexible class called the Marshall-Olkin odd Burr III family for generating continuous distributions and derive some of its statistical properties. We provide three special models which accommodate symmetrical, right-skewed and left-skewed shaped densities as well as bathtub, decreasing, increasing, reversed-J shaped and upside-down bathtub failure rate functions. The parameters are estimated by maximum likelihood, least squares and a percentile method. Some simulations investigate the accuracy of the three methods. We illustrate the utility of a special model through three applications to engineering field.

The current investigation is the first report of utilization of Aspergillus brasiliensis for the decolorization of melanoidin in distillery effluent. The effluent generated from alcohol distilleries is one of the most complex wastewater with a high biological oxygen demand (BOD) and other organic, inorganic, and toxic constituents. The effluent contains melanoidin, a dark brown compound, which is difficult to remediate by using conventional technologies. The disposal of spent wash in the natural environment is hazardous and can deteriorate land and water resources. The decolorization of spent wash through physical and chemical methods remains unsuitable, and the only alternative to decolorize spent wash is biological treatment. In the current study, three fungal strains were isolated from the distillery waste and screened for their ability to decolorize melanoidin.The isolate RS2 exhibited maximum decolorization of 83% and was identified as Aspergillus brasiliensis. Its optimum growth temperature was 37EC, and the maximum efficiency was recorded after 120 h of incubation. Nutritional sources were investigated for the fungi showing the maximum decolorization of melanoidin, and starch and peptone were found to be the best carbon and nitrogen sources, respectively. At 1.5% starch concentration and 1.5% peptone concentration, the decolorization level attained was 87.45% and 88.74%, respectively. A. brasiliensis exhibited a high potential to decolorize melanoidin. The decolorization percentage was high, which makes this fungus a potential candidate for use at the industrial scale for the bioremediation of spent wash.

In this article, a new "odds generalized gamma-G" family of distributions, called the GG-G family of distributions, is introduced. We propose a complete mathematical and statistical study of this family, with a special focus on the Fréchet distribution as baseline distribution. In particular, we provide infinite mixture representations of its probability density function and its cumulative distribution function, the expressions for the Rényi entropy, the reliability parameter and the probability density function of ith order statistic. Then, the statistical properties of the family are explored. Model parameters are estimated by the maximum likelihood method. A regression model is also investigated. A simulation study is performed to check the validity of the obtained estimators. Applications on real data sets are also included, with favorable comparisons to existing distributions in terms of goodness-of-fit.

Seed purity is an important indicator of crop seed quality. On the other side, corn is an important crop of the modern agricultural industry with more than 40% grain Worldwide production. The purpose of this study was to examine the feasibility of a machine learning (ML) approach for classifying different types of corn seeds. The seed digital images (DI) of six corn varieties were Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, and ICI 339. This was achieved through a digital camera in a natural environment without a complicated laboratory system. The acquired DI dataset converted to a hybrid feature dataset, which is the combination of histogram, texture, and spectral features. For each corn seed image, a total of fifty-five hybrid-features was acquired on every non-overlapping region of interest (ROI), sizes (75 × 75), (100 × 100), (125 × 125) and (150 × 150). The nine optimized features have been acquired by employing the correlation-based feature selection (CFS) technique with the Best First search algorithm. To build the classification models, Random forest (RF), BayesNet (BN), LogitBoost (LB), and Multilayer Perceptron (MLP) were employed using optimized multi-feature using (10-fold) cross-validation approach. A comparative analysis of four ML classifiers, the MLP performed outstanding classification accuracy (98.93%), on ROIs size (150 × 150). The accuracy values by MLP on six corn seed verities named Desi Makkai, Sygenta ST-6142, Kashmiri Makkai, Pioneer P-1429, Neelam Makkai, ICI- 339 was 99.8%, 97%, 98.5%, 98.6%, 99.9%, and 99.4%, respectively.

In this paper, a new modified Kumaraswamy distribution is proposed, and some of its basic properties are presented, such as the mathematical expressions for the moments, probability weighted moments, order statistics, quantile function, reliability, and entropy measures. The parameter estimation is done via the maximum likelihood estimation method. In order to show the usefulness of the proposed model, some well-established actuarial measures such as value-at-risk, expected-shortfall, tail-value-at-risk, tail-variance, and tail-variance-premium are obtained. A simulation study is carried out to assess the performance of maximum likelihood estimates. The empirical analysis is carried out to show that our proposed model is better in performance as compared to other competitive models related to the extended Kumaraswamy model. Thus, insurance claim data and engineering related real-life data sets are considered to prove this claim.

In this paper, a new distribution named as unit-power Weibull distribution (UPWD) defined on interval (0,1) is introduced using an appropriate transformation to the positive random variable of the Weibull distribution. This work offers quantile function, linear representation of the density, ordinary and incomplete moments, moment-generating function, probability-weighted moments, L-moments, TL-moments, Rényi entropy, and MLE estimation. Additionally, several actuarial measures are computed. The real data applications are carried out to underline the practical usefulness of the model. In addition, a bivariate extension for the univariate power Weibull distribution named as bivariate unit-power Weibull distribution (BIUPWD) is also configured. To elucidate the bivariate extension, simulation analysis and application using COVID-19-associated fatality rate data from Italy and Belgium to conform a BIUPW distribution with visual depictions are also presented.

In the present study, the group acceptance plan is examined when the lifetime of an item follows the odd Perks exponential distribution, and a large number of items regarded as a group are evaluated simultaneously. The crucial parameters are derived from the consumer risk and the test termination period. The operating characteristics function values are generated for various quality levels. An optimized group acceptance plan and comparison of group acceptance sampling plan with the ordinary sampling plan are also presented. Additionally, a graphical illustration of operating characteristics for diverse groups and parametric values is provided. The minimum ratios of the actual average life to the stipulated average life are likewise computed at the prescribed producer’s risk. Examples are used to illustrate the outcomes via our algorithm under the odd Perks exponential distribution setting. It is explained using a quality control dataset to establish its practical versatility.

Forecasting has been playing an important role in different fields of life, i.e., in decision-making activities of management, to predict uncertain events within an organization, in weather forecasting, in flood forecasting, etc. Stakeholders involved in betting market take advantage of tennis forecasting directly or indirectly. Winning probability calculated using forecasting models helps the bettors in deciding whether to place a bet or not. Keeping in view the importance of tennis forecasting, the Bradley-Terry model is used to model men’s professional tennis for predicting match outcomes in tennis matches of men’s singles. Model coefficients are estimated using data from January 2019 to September 2020 of 3439 matches. Ratings for each player are calculated using model coefficients. Player rankings are then calculated using these ratings. Comparison of model rankings with ATP rankings has shown satisfactory results. Winning probability for each player is calculated using model coefficients and ratings. These probability predictions are evaluated against four measures of performance. The results reveal that surface on which a game is played on contributes significantly towards a player’s performance. Due to this impact of the surface, our model has produced superior player rankings for certain players who had been ranked very low in official ATP rankings. According to most of the performance measures, the model has shown good results for clay court data which are closely followed by hard court data. To calculate return on investment, model results are compared with the bookmakers’ average odds and best available odds. It has been found that return on investment for a fitted model is highest in the case of clay court data in comparison to bookmaker’s average odds and best odds.

A robust generalisation of the Gumbel distribution is proposed in this article. This family of distributions is based on the T-X paradigm. From a list of special distributions that have evolved as a result of this family, three separate models are also mentioned in this article. A linear combination of generalised exponential distributions can be used to characterise the density of a new family, which is critical in assessing some of the family's properties. The statistical features of this family are determined, including exact formulations for the quantile function, ordinary and incomplete moments, generating function, and order statistics. The model parameters are estimated using the maximum likelihood method. Further, one of the unique models has been systematically studied. Along with conventional skewness measures, MacGillivray skewness is also used to quantify the skewness measure. The new probability distribution also enables us to determine certain critical risk indicators, both numerically and graphically. We use a simulated assessment of the suggested distribution, as well as apply three real-world data sets in modelling the proposed model, in order to ensure its authenticity and superiority.

One of the most used techniques for determining animal abundance is distance sampling. The distance sampling framework depends on the idea of a detection function, and a number of options have been suggested. In this paper, we provide a new flexible parametric model based on the Burr XII distribution. To be more specific, we use the survival function of the Burr XII distribution for novel purposes in this context. The proposed model is appealing because it meets all of the requirements for a reliable detection function model, such as being monotonically decreasing and having a shoulder at the origin. It also has the features of having various asymmetric properties and a heavy right tail, which are rare properties in this setting. In the first part, we provide its key characteristics, such as shapes and moments. Then, the inferential aspect of the model is investigated. The maximum likelihood estimation method is used to estimate the parameters in a data-fitting scenario. The estimates of population abundance are derived and compared with some existing parametric estimates. A simulation is run to assess how well the resulting estimates perform in comparison to other widely applied estimates from the literature. The model is then tested using two real-world data sets. Based on the famous goodness-of-fit statistics, we show that it is preferable to some of the well-established models.

In this paper, we develop the new extended Kumaraswamy generated (NEKwG) family of distributions. It aims to improve the modeling capability of the standard Kumaraswamy family by using a one-parameter exponential-logarithmic transformation. Mathematical developments of the NEKwG family are provided, such as the probability density function series representation, moments, information measure, and order statistics, along with asymptotic distribution results. Two special distributions are highlighted and discussed, namely, the new extended Kumaraswamy uniform (NEKwU) and the new extended Kumaraswamy exponential (NEKwE) distributions. They differ in support, but both have the features to generate models that accommodate versatile skewed data and non-monotone failure rates. We employ maximum likelihood, least-squares estimation, and Bayes estimation methods for parameter estimation. The performance of these methods is discussed using simulation studies. Finally, two real data applications are used to show the flexibility and importance of the NEKwU and NEKwE models in practice.

In this paper, we introduce a truncated general-G class of distributions. This class can be viewed as a weighted family of distributions with a general weight function, and also it generalizes the beta generator family proposed by Eugene et al. (2002). Some features of the class are stated with a comprehensive study to the truncated Burr-G (TB-G) family as one of the important subclass of the introduced class. The study includes the mixture representation in terms of baseline distribution, moments, moment generating function, stochastic ordering, stress-strength parameter, entropies, estimation by the maximum likelihood. The applicability of some new sub-models of the TB-G family is shown using two practical data sets.

The identification of genes that are expressed differentially in the diseased versus healthy individual give relevant information regarding the pathology of the disease. The identification of DEGs can be a significant step in the field of clinical and pharmaceutical research. They can act as a potent biomarker, therapeutic target, or gene signature for the early diagnosis of the disease. Intellectual disability is a neurodevelopmental disorder that affects those at the fetal stage. Timely diagnosis of the disease can help in preventing severe neurodevelopmental delay in the child. In the current study, a meta-analysis approach was applied for the identification of the DEGs in patients of intellectual disability disorder. Six intellectual disability datasets were retrieved from the GEO database of NCBI and were subjected to quality check, trimming, and alignment. Post-alignment, FeatureCounts was used to form a raw gene count file for differential analysis. The differentially expressed genes were analyzed using the EdgeR statistical package of R Studio. The genes which had an FDR p-value less than 0.05 and log2foldchange greater than 0 were considered upregulated and significantly expressed genes. The study found MTRNR2L1, PAPSS2, L1CAM, IGLV1-47, IGLV3-19, and IGKV1-16 genes to be upregulated in the patient sample. These genes can thus play an important role in the progression of intellectual disability disorder that facilitates early diagnosis of the disease.

In this paper, a new discrete distribution called Binomial–Natural Discrete Lindley distribution is proposed by compounding the binomial and natural discrete Lindley distributions. Some properties of the distribution are discussed including the moment-generating function, moments and hazard rate function. Estimation of the distribution’s parameter is studied by methods of moments, proportions and maximum likelihood. A simulation study is performed to compare the performance of the different estimates in terms of bias and mean square error. SO2 data applications are also presented to see that the new distribution is useful in modeling data.

Circular RNAs (circRNAs) are an evolutionarily conserved novel class of non-coding endogenous RNAs (ncRNAs) found in the eukaryotic transcriptome, originally believed to be aberrant RNA splicing by-products with decreased functionality. However, recent advances in high-throughput genomic technology have allowed circRNAs to be characterized in detail and revealed their role in controlling various biological and molecular processes, the most essential being gene regulation. Because of the structural stability, high expression, availability of microRNA (miRNA) binding sites and tissue-specific expression, circRNAs have become hot topic of research in RNA biology. Compared to the linear RNA, circRNAs are produced differentially by backsplicing exons or lariat introns from a pre-messenger RNA (mRNA) forming a covalently closed loop structure missing 3′ poly-(A) tail or 5′ cap, rendering them immune to exonuclease-mediated degradation. Emerging research has identified multifaceted roles of circRNAs as miRNA and RNA binding protein (RBP) sponges and transcription, translation, and splicing event regulators. CircRNAs have been involved in many human illnesses, including cancer and neurodegenerative disorders such as Alzheimer’s and Parkinson’s disease, due to their aberrant expression in different pathological conditions. The functional versatility exhibited by circRNAs enables them to serve as potential diagnostic or predictive biomarkers for various diseases. This review discusses the properties, characterization, profiling, and the diverse molecular mechanisms of circRNAs and their use as potential therapeutic targets in different human malignancies.

In recent years, the trigonometric families of continuous distributions have found a place of choice in the theory and practice of statistics, with the Sin-G family as leader. In this paper, we provide some contributions to the subject by introducing a flexible extension of the Sin-G family, called the transformed Sin-G family. It is constructed from a new polynomial-trigonometric function presenting a desirable "versatile concave/convex" property, among others. The modelling possibilities of the former Sin-G family are thus multiplied. This potential is also highlighted by a complete theoretical work, showing stochastic ordering results, studying the analytical properties of the main functions, deriving several kinds of moments, and discussing the reliability parameter as well. Then, the applied side of the proposed family is investigated, with numerical results and applications on the related models. In particular, the estimation of the unknown model parameters is performed through the use of the maximum likelihood method. Then, two real life data sets are analyzed by a new extended Weibull model derived to the considered trigonometric mechanism. We show that it performs the best among seven comparable models, illustrating the importance of the findings.

In recent years, the Muth distribution has been used for the construction of accurate statistical models, with applications in various applied fields. In this paper, we use a truncated-composed scheme to create a new unit Muth distribution, from which we motivate a more general family of continuous distributions called the truncated Muth generated family. The key benefits of this family are its analytical simplicity, connections with the exponential generated family, and flexibility conferred on any parental distribution. In particular, it improves the capability of the functions of the parental distribution, enhancing their peak, asymmetry, tail, and flatness levels, among others. The characteristics of quantile and moment measures and functions of the truncated Muth generated family are described in detail. As a concrete example, a particular distribution that extends the Weibull distribution is highlighted. In an applied part, the parameters are calculated using the maximum likelihood procedure. We use a comprehensive simulation analysis to demonstrate the accuracy of the derived estimates. The revised Weibull model is then used to fit two real-world datasets. The new model is shown to be more suited to these datasets than other competing models.

A new family of continuous distributions called the generalized odd linear exponential family is proposed. The probability density and cumulative distribution function are expressed as infinite linear mixtures of exponentiated-F distribution. Important statistical properties such as quantile function, moment generating function, distribution of order statistics, moments, mean deviations, asymptotes and the stress–strength model of the proposed family are investigated. The maximum likelihood estimation of the parameters is presented. Simulation is carried out for two of the mentioned sub-models to check the asymptotic behavior of the maximum likelihood estimates. Two real-life data sets are used to establish the credibility of the proposed model. This is achieved by conducting data fitting of two of its sub-models and then comparing the results with suitable competitive lifetime models to generate conclusive evidence.

Background: Humans can deliver many emotions during a conversation. Facial expressions show information about emotions. Objectives: This study proposed a Machine Learning (ML) approach based on a statistical analysis of emotion recognition using facial expression through a digital image. Methodology: A total of 600 digital image datasets divided into 6 classes (Anger, Happy, Fear, Surprise, Sad, and Normal) was collected from publicly available Taiwan Facial Expression Images Database. In the first step, all images are converted into a gray level format and 4 Regions of Interest (ROIs) are created on each image, so the total image dataset gets divided in 2400 (600 x 4) sub-images. In the second step, 3 types of statistical features named texture, histogram, and binary feature are extracted from each ROIs. The third step is a statistical feature optimization using the best-first search algorithm. Lastly, an optimized statistical feature dataset is deployed on various ML classifiers. Results: The analysis part was divided into two phases: firstly boosting algorithms-based ML classifiers (named as LogitBoost, AdaboostM1, and Stacking) which obtained 94.11%, 92.15%, and 89.21% accuracy, respectively. Secondly, decision tree algorithms named J48, Random Forest, and Random Committee were obtained with 97.05%, 93.14%, and 92.15% accuracy, respectively. Conclusion: It was observed that decision tree based J48 classifiers gave 97.05% classification accuracy.

In this paper, we introduce the exponentiated power generalized Weibull power series (EPGWPS) family of distributions, obtained by compounding the exponentiated power generalized Weibull and power series distributions. By construction, the new family contains a myriad of new flexible lifetime distributions having strong physical interpretations (lifetime system, biological studies…). We discuss the characteristics and properties of the EPGWPS family, including its probability density and hazard rate functions, quantiles, moments, incomplete moments, skewness and kurtosis. The main vocation of the EPGWPS family remains to be applied in a statistical setting, and data analysis in particular. In this regard, we explore the estimation of the model parameters by the maximum likelihood method, with accuracy supported by a detailed simulation study. Then, we apply it to two practical data sets, showing the applicability and competitiveness of the EPGWPS models in comparison to some other well-reputed models.